The effective physical data sharing has been facilitating the functionality of Industrial IoTs, which is believed to be one primary basis for Industry 4.0. These physical data, while providing pivotal information for multiple components of a production system, also bring in severe privacy issues for both workers and manufacturers, thus aggravating the challenges for data sharing. Current designs tend to simplify the behaviors of participants for better theoretical analysis, and they cannot properly handle the challenges in IIoTs where the behaviors are more complicated and correlated. Therefore, this paper proposes a privacy-preserved data sharing framework for IIoTs, where multiple competing data consumers exist in different stages of the system. The framework allows data contributors to share their contents upon requests. The uploaded contents will be perturbed to preserve the sensitive status of contributors. The differential privacy is adopted in the perturbation to guarantee the privacy preservation. Then the data collector will process and relay contents with subsequent data consumers. This data collector will gain both its own data utility and extra profits in data relay. Two algorithms are proposed for data sharing in different scenarios, based on whether the service provider will further process the contents to retain its exclusive utility. This work also provides for both algorithms a comprehensive consideration on privacy, data utility, bandwidth efficiency, payment, and rationality for data sharing. Finally, the evaluation on real-world datasets demonstrates the effectiveness of proposed methods, together with clues for data sharing towards Industry 4.0.

Privacy-Preserved Data Sharing Towards Multiple Parties in Industrial IoTs

Security, privacy and trust of different layers in Internet-of-Things (IoTs) framework

An Efficient Algorithm for Media-based Surveillance System (EAMSuS) in IoT Smart City Framework

Android security incidents occurred frequently in recent years. To improve the accuracy and efficiency of large-scale Android malware detection, in this work, we propose a hybrid model based on deep autoencoder (DAE) and convolutional neural network (CNN). First, to improve the accuracy of malware detection, we reconstruct the high-dimensional features of Android applications (apps) and employ multiple CNN to detect Android malware. In the serial convolutional neural network architecture (CNN-S), we use Relu, a non-linear function, as the activation function to increase sparseness and “dropout” to prevent over-fitting. The convolutional layer and pooling layer are combined with the full-connection layer to enhance feature extraction capability. Under these conditions, CNN-S shows powerful ability in feature extraction and malware detection. Second, to reduce the training time, we use deep autoencoder as a pre-training method of CNN. With the combination, deep autoencoder and CNN model (DAE-CNN) can learn more flexible patterns in a short time. We conduct experiments on 10,000 benign apps and 13,000 malicious apps. CNN-S demonstrates a significant improvement compared with traditional machine learning methods in Android malware detection. In details, compared with SVM, the accuracy with the CNN-S model is improved by 5%, while the training time using DAE-CNN model is reduced by 83% compared with CNN-S model.

Effective android malware detection with a hybrid model based on deep autoencoder and convolutional neural network

As an enabler for smart industrial Internet of Things (IoT), sensor cloud facilitates data collection, processing, analysis, storage, and sharing on demand. However, compromised or malicious sensor nodes may cause the collected data to be invalid or even endanger the normal operation of an entire IoT system. Therefore, designing an effective mechanism to ensure the trustworthiness of sensor nodes is a critical issue. However, existing cloud computing models cannot provide direct and effective management for the sensor nodes. Meanwhile, the insufficient computation and storage ability of sensor nodes makes them incapable of performing complex intelligent algorithms. To this end, mobile edge nodes with relatively strong computation and storage ability are exploited to provide intelligent trust evaluation and management for sensor nodes. In this article, a mobile edge computing-based intelligent trust evaluation scheme is proposed to comprehensively evaluate the trustworthiness of sensor nodes using probabilistic graphical model. The proposed mechanism evaluates the trustworthiness of sensor nodes from data collection and communication behavior. Moreover, the moving path for the edge nodes is scheduled to improve the probability of direct trust evaluation and decrease the moving distance. An approximation algorithm with provable performance is designed. Extensive experiments validate that our method can effectively ensure the trustworthiness of sensor nodes and decrease the energy consumption.

MTES: An Intelligent Trust Evaluation Scheme in Sensor-Cloud-Enabled Industrial Internet of Things

Recently, H.265/MPEG-H or high efficiency video coding (HEVC) as it is well known, has been established as better compression standard due to reduction of about 50 % bit-rate for same video quality and less bandwidth consumption, compared to its predecessor H.264/MPEG-advanced video coding standard. Many algorithms have been proposed and developed for efficient and secure streaming of multimedia files. However, these methods do not meet all the requirements of effective and secure transmission over the internet. In this paper, we present a new encryption and transmission algorithm for efficient HEVC delivery. Experimental results demonstrate that our proposed algorithm is more secure and effective compared to previous algorithms used for H.264 standard and shows better overall performance.

Encryption algorithm for efficient transmission of HEVC media

Nowadays, the traditional teacher-centered way of teaching seems to be anachronistic. Disabled people, and not only, face difficulties to follow the current educational methods. The evolution of technology can offer significant benefits in these problems. Cutting-edge technologies, such as as Internet of Things (IoT), Cloud Computing (CC), Wireless Sensor Networks (WSN), Big Data Analytics (BDA), Compressed Sensing (CS), Augmented Reality/Virtual Reality (AR/VR) and 5G Networking can contribute in the field of smart education. However, smart education is currently in an embryonic 2D data representational state. In this paper, we propose a new Revolutionary Interactive Smart Classroom (RISC) which will provide a virtual environment for enhanced learning experience, based on 5G Network. This classroom will also make use of 3D virtual services in combination with haptic equipment and sensors, in order to carry out augmented human sensing information and touch into the virtual class. The advantages of such a classroom will be the sustainability in many fields, such as Society, Education, Environment, Economics, Technology and Cultural Tourism.

A Revolutionary Interactive Smart Classroom (RISC) with the Use of Emerging Technologies

The daily rapid malware growth and spread has enforced the security community of antivirus companies to introduce cloud computing technology to their existing protection methods so as to be able to deal with efficiently the active malware threats.
 A new hybrid security model, based on cloud computing, should be developed to offer optimized protection to the connected users. In this research, we describe our proposed cloud infrastructure and analyze it with mathematical models to export significant diagrams about various metrics. Our cloud model architecture consists of four layers: the master cloud server, the slave servers, the virtual subservers and the users connected to the cloud. Experimental results demonstrate that our proposed layered cloud architecture verifies the trust of its implementation and establishment, due to the fact that it makes the current architecture more lightweight, efficient and secure for e-health data transmission.

An Enhanced and Secure Cloud Infrastructure for e-Health Data Transmission

Internet of Things (IoT) is a revolutionary expandable network which has brought many advantages, improving the Quality of Life (QoL) of individuals. However, IoT carries dangers, due to the fact that hackers have the ability to find security gaps in users’ IoT devices, which are not still secure enough and hence, intrude into them for malicious activities. As a result, they can control many connected devices in an IoT network, turning IoT into Botnet of Things (BoT). In a botnet, hackers can launch several types of attacks, such as the well known attacks of Distributed Denial of Service (DDoS) and Man in the Middle (MitM), and/or spread various types of malicious software (malware) to the compromised devices of the IoT network. In this paper, we propose a novel hybrid Artificial Intelligence (AI)-powered honeynet for enhanced IoT botnet detection rate with the use of Cloud Computing (CC). This upcoming security mechanism makes use of Machine Learning (ML) techniques like the Logistic Regression (LR) in order to predict potential botnet existence. It can also be adopted by other conventional security architectures in order to intercept hackers the creation of large botnets for malicious actions.

Vasileios A. Memos

Papers

An Efficient Algorithm for Media-based Surveillance System (EAMSuS) in IoT Smart City Framework

Encryption algorithm for efficient transmission of HEVC media

A Revolutionary Interactive Smart Classroom (RISC) with the Use of Emerging Technologies

An Enhanced and Secure Cloud Infrastructure for e-Health Data Transmission

AI-Powered Honeypots for Enhanced IoT Botnet Detection